Java tutorial
/** * Licensed to the Apache Software Foundation (ASF) under one or more * contributor license agreements. See the NOTICE file distributed with * this work for additional information regarding copyright ownership. * The ASF licenses this file to You under the Apache License, Version 2.0 * (the "License"); you may not use this file except in compliance with * the License. You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ package io.ssc.trackthetrackers.analysis.stats; import com.google.common.base.Preconditions; import com.google.common.collect.Multiset; import com.google.common.collect.Ordering; import java.util.ArrayList; import java.util.Collections; import java.util.List; import java.util.PriorityQueue; import java.util.Queue; /** * Utility methods for working with log-likelihood */ public final class LogLikelihood { private LogLikelihood() { } /** * Calculates the unnormalized Shannon entropy. This is * * -sum x_i log x_i / N = -N sum x_i/N log x_i/N * * where N = sum x_i * * If the x's sum to 1, then this is the same as the normal * expression. Leaving this un-normalized makes working with * counts and computing the LLR easier. * * @return The entropy value for the elements */ public static double entropy(long... elements) { long sum = 0; double result = 0.0; for (long element : elements) { Preconditions.checkArgument(element >= 0); result += xLogX(element); sum += element; } return xLogX(sum) - result; } private static double xLogX(long x) { return x == 0 ? 0.0 : x * Math.log(x); } /** * Merely an optimization for the common two argument case of {@link #entropy(long...)} * @see #logLikelihoodRatio(long, long, long, long) */ private static double entropy(long a, long b) { return xLogX(a + b) - xLogX(a) - xLogX(b); } /** * Merely an optimization for the common four argument case of {@link #entropy(long...)} * @see #logLikelihoodRatio(long, long, long, long) */ private static double entropy(long a, long b, long c, long d) { return xLogX(a + b + c + d) - xLogX(a) - xLogX(b) - xLogX(c) - xLogX(d); } /** * Calculates the Raw Log-likelihood ratio for two events, call them A and B. Then we have: * <p/> * <table border="1" cellpadding="5" cellspacing="0"> * <tbody><tr><td> </td><td>Event A</td><td>Everything but A</td></tr> * <tr><td>Event B</td><td>A and B together (k_11)</td><td>B, but not A (k_12)</td></tr> * <tr><td>Everything but B</td><td>A without B (k_21)</td><td>Neither A nor B (k_22)</td></tr></tbody> * </table> * * @param k11 The number of times the two events occurred together * @param k12 The number of times the second event occurred WITHOUT the first event * @param k21 The number of times the first event occurred WITHOUT the second event * @param k22 The number of times something else occurred (i.e. was neither of these events * @return The raw log-likelihood ratio * * <p/> * Credit to http://tdunning.blogspot.com/2008/03/surprise-and-coincidence.html for the table and the descriptions. */ public static double logLikelihoodRatio(long k11, long k12, long k21, long k22) { Preconditions.checkArgument(k11 >= 0 && k12 >= 0 && k21 >= 0 && k22 >= 0); // note that we have counts here, not probabilities, and that the entropy is not normalized. double rowEntropy = entropy(k11 + k12, k21 + k22); double columnEntropy = entropy(k11 + k21, k12 + k22); double matrixEntropy = entropy(k11, k12, k21, k22); if (rowEntropy + columnEntropy < matrixEntropy) { // round off error return 0.0; } return 2.0 * (rowEntropy + columnEntropy - matrixEntropy); } /** * Calculates the root log-likelihood ratio for two events. * See {@link #logLikelihoodRatio(long, long, long, long)}. * @param k11 The number of times the two events occurred together * @param k12 The number of times the second event occurred WITHOUT the first event * @param k21 The number of times the first event occurred WITHOUT the second event * @param k22 The number of times something else occurred (i.e. was neither of these events * @return The root log-likelihood ratio * * <p/> * There is some more discussion here: http://s.apache.org/CGL * * And see the response to Wataru's comment here: * http://tdunning.blogspot.com/2008/03/surprise-and-coincidence.html */ public static double rootLogLikelihoodRatio(long k11, long k12, long k21, long k22) { double llr = logLikelihoodRatio(k11, k12, k21, k22); double sqrt = Math.sqrt(llr); if ((double) k11 / (k11 + k12) < (double) k21 / (k21 + k22)) { sqrt = -sqrt; } return sqrt; } /** * Compares two sets of counts to see which items are interestingly over-represented in the first * set. * @param a The first counts. * @param b The reference counts. * @param maxReturn The maximum number of items to return. Use maxReturn >= a.elementSet.size() to return all * scores above the threshold. * @param threshold The minimum score for items to be returned. Use 0 to return all items more common * in a than b. Use -Double.MAX_VALUE (not Double.MIN_VALUE !) to not use a threshold. * @return A list of scored items with their scores. */ public static <T> List<ScoredItem<T>> compareFrequencies(Multiset<T> a, Multiset<T> b, int maxReturn, double threshold) { int totalA = a.size(); int totalB = b.size(); Ordering<ScoredItem<T>> byScoreAscending = new Ordering<ScoredItem<T>>() { @Override public int compare(ScoredItem<T> tScoredItem, ScoredItem<T> tScoredItem1) { return Double.compare(tScoredItem.score, tScoredItem1.score); } }; Queue<ScoredItem<T>> best = new PriorityQueue<ScoredItem<T>>(maxReturn + 1, byScoreAscending); for (T t : a.elementSet()) { compareAndAdd(a, b, maxReturn, threshold, totalA, totalB, best, t); } // if threshold >= 0 we only iterate through a because anything not there can't be as or more common than in b. if (threshold < 0) { for (T t : b.elementSet()) { // only items missing from a need be scored if (a.count(t) == 0) { compareAndAdd(a, b, maxReturn, threshold, totalA, totalB, best, t); } } } List<ScoredItem<T>> r = new ArrayList<ScoredItem<T>>(best); Collections.sort(r, byScoreAscending.reverse()); return r; } private static <T> void compareAndAdd(Multiset<T> a, Multiset<T> b, int maxReturn, double threshold, int totalA, int totalB, Queue<ScoredItem<T>> best, T t) { int kA = a.count(t); int kB = b.count(t); double score = rootLogLikelihoodRatio(kA, totalA - kA, kB, totalB - kB); if (score >= threshold) { ScoredItem<T> x = new ScoredItem<T>(t, score); best.add(x); while (best.size() > maxReturn) { best.poll(); } } } public static final class ScoredItem<T> { private final T item; private final double score; public ScoredItem(T item, double score) { this.item = item; this.score = score; } public double getScore() { return score; } public T getItem() { return item; } } }